WDR62 affects the progression of ovarian cancer by regulating the cell cycle.

Background: Ovarian Cancer (OC) is a gynecological malignant tumor with an extremely high mortality rate, seriously endangering women's health. Due to its insidious clinical manifestations, most patients are diagnosed in the advanced stage of the disease. The currently clinically relied CA125 has limited specificity for the early diagnosis of ovarian cancer.

MFI-Net: multi-level feature invertible network image concealment technique.

The utilization of deep learning and invertible networks for image hiding has been proven effective and secure. These methods can conceal large amounts of information while maintaining high image quality and security. However, existing methods often lack precision in selecting the hidden regions and primarily rely on residual structures.

GU-Net: Causal relationship-based generative medical image segmentation model.

Due to significant anatomical variations in medical images across different cases, medical image segmentation is a highly challenging task. Convolutional neural networks have shown faster and more accurate performance in medical image segmentation. However, existing networks for medical image segmentation mostly rely on independent training of the model using data samples and loss functions, lacking interactive training and feedback mechanisms.

MEF2A, a gene associated with mitochondrial biogenesis, promotes drug resistance in gastric cancer.

Chemotherapeutic resistance is a major obstacle to the effectiveness of cisplatin-based chemotherapy for gastric cancer (GC), leading to treatment failure and poor survival rates. However, the underlying mechanisms are not fully understood. Our study demonstrated that the transcription factor myocyte enhancer factor 2A (MEF2A) plays a role in chemotherapeutic drug resistance by regulating the transcription of PGC1α and KEAP1, promoting mitochondrial biogenesis.

Positioning in 8q24.21 Involved in the Tumorigenesis of Colorectal Cancer by Targeting .

Background: Colorectal cancer remains to be the third leading cause of cancer mortality rates. Despite the diverse effects of the miRNA cluster located in of 8q24.21 across various tumors, the specific biological function in colorectal cancer has not been clarified.

EA-Net: Research on skin lesion segmentation method based on U-Net.

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making.